The role of physiological noise in resting-state functional connectivity

By Rasmus M. Birn. NeuroImage, 2012

378 citations recorded by the source corpus.

Functional connectivity between different brain regions can be estimated from MRI data by computing the temporal correlation of low frequency (<0.1Hz) fluctuations in the MRI signal. These correlated fluctuations occur even when the subject is “at rest” (not asked to perform any particular task) and result from spontaneous neuronal activity synchronized within multiple distinct networks of brain regions. This estimate of connectivity, however, can be influenced by physiological noise, such as cardiac and respiratory fluctuations. This brief review looks at the effect of physiological noise on estimates of resting-state functional connectivity, discusses ways to remove physiological noise, and provides a personal recollection of the early developments in these approaches. This review also discusses the importance of physiological noise correction and provides a summary of evidence demonstrating that functional connectivity does have a neuronal underpinning and cannot purely be the result of physiological noise.

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/33a0623406112ca9ea925e2e67af6cb24ef4b578

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In Cardiology and Cardiovascular Medicine

In Cognitive Neuroscience

Citations in the corpus

The Human Connectome Project’s Neuroimaging Approach (2016, neutral): “111–113).”
An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI (2017, background): “GSR has been shown to reduce non-neuronal sources of physiological variance in the BOLD signal, such as those linked to respiration (Birn, 2012; Power et al., 2017b), and to mitigate the effects of in-scanner movement (Power et al.”
DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging (2016, methodology): “The R-fMRI field has been expanding exponentially, with more than 1,000 studies now being published per year (Birn, 2012; Yan et al., 2014; Zuo and Xing, 2014).”
The effect of scan length on the reliability of resting-state fMRI connectivity estimates (2013, neutral): “These signal fluctuations from non-neuronal activity may 75 overestimate the strength of connectivity and, in turn, affect the reliabil- 76 ity of estimating ICNs (Birn, 2012; Power et al., 2012; Weissenbacher 77 et al., 2009).”
Methods for cleaning the BOLD fMRI signal (2016, positive): “Compared with previous reviews, which focused more on a particular confound or type of fMRI data (e.g. Birn, 2012; Murphy et al., 2013, Power et al., 2015), our goal was to give a more methodological point of view about the operation of the different techniques, pointing out the advantages and…”